…use data, AI and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030.
Stakeholder: the public (as individual stakeholders), local government
In the UK a household is classed as deprived by the Office for National Statistics (2011) if it meets one or more of the following criteria:
Employment: Where any member of a household, who is not a full-time student, is either unemployed or long-term sick.
Education: No person in the household has at least Level 2 education (see highest level of qualification), and no person aged 16 to 18 is a full-time student.
Health and disability: Any person in the household has general health that is ‘bad’ or ‘very bad’ or has a long-term health problem.
Housing: The household’s accommodation is either overcrowded, with an occupancy rating -1 or less, or is in a shared dwelling, or has no central heating.
Studies show that children in the most deprived areas are nearly twice as likely to be obese when compared to those in the least deprived areas. Obesity and the associated health issues are estimated to have cost the NHS £4.2 billion in 2007 (Baker, 2019). This figure is expected to rise to reach a figure of £9.7 billion by 2050. This only encapsulates the cost the NHS and not the financial impact upon other services, such as social care and other economic consequences (Baker, 2019).
Children who are obese a that age are more likely to be obese as adults. (WHO, 2019) By 2017, in the UK, approximately one third of children between the ages of 2 and 15 were overweight or obese (Health and Social Care Information Centre, 2015). Children are becoming obese at an increasingly young age and remaining obese for longer than previous generations (Johnson et al, 2015). They are more likely to suffer from poor levels of self-esteem and be subject to bullying, in addition to suffering from consequent diseases such as heart disease and type 2 diabetes (Public Health England, 2018).
The reduction of obesity levels is crucial as obesity doubles the risk of premature death and in addition to the risk of physical diseases, individuals are likely to live with mental health conditions such as depression (Gatineau and Dent, 2011). Access to parks and green spaces has been shown to reduce mortality as well as risk of various chronic diseases.
Findings suggest that the proximity to a park may help promote population physical activity, when not hindered in access by major roadways, and has been linked to positive human health outcomes. Especially in urban areas, open spaces are valued by theorists who relate them to the social, political, and physical health of residents and communities. Some argue that high-quality, pedestrian-friendly neighborhood spaces can engender beneficial interpersonal connections. However, in deprived areas, the use of green spaces can be discouraged by either low accessibility or by anti-social behaviour which is off-putting parents from visiting parks with their children (Edwards et al., 2014; Wolch et al., 2014; Timperio et al., 2005).
To address the above issues, we therefore suggest a combined approach to evaluate the efficiency of local council’s intervention to tackle deprivation factors. For instance, in an urban context, practitioners have tended to address the issue of high rates of chronic diseases by implementing green areas that may have been better used to promote outdoor activities, to result in a reduction of obesity especially in children. However, in our preliminary investigation, we did not find a sufficiently significant relation between the presence of urban green areas (mainly parks) or air pollution index and childhood obesity, most likely due to the aggregation of data by ward or region size.
Using Child Obesity data (10-11 years) as representation of chronic disease, can we model which deprivation factors are prevalent per area in order to inform the government of areas of efficient investment, as well as the public on how to facilitate healthy lifestyle choices?
library(plotly)
library(knitr)
library(tidyverse)
library(sf)
library(here)
library(fingertipsR)
library(tmap)
tmap_mode("view")wards <- st_read(here("data", "raw", "Wards_December_2018_Generalised_Clipped_Boundaries_UK", "Wards_December_2018_Generalised_Clipped_Boundaries_UK.shp")) %>%
select(wd18cd, wd18nm, st_areasha) %>%
st_transform(27700)## Reading layer `Wards_December_2018_Generalised_Clipped_Boundaries_UK' from data source `C:\Users\diego\Documents\000000000000_WORK\00000_SSASM\TeamB\data\raw\Wards_December_2018_Generalised_Clipped_Boundaries_UK\Wards_December_2018_Generalised_Clipped_Boundaries_UK.shp' using driver `ESRI Shapefile'
## Simple feature collection with 9114 features and 10 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -116.1928 ymin: 5342.7 xmax: 655644.8 ymax: 1220302
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defsobese_year6_pct <- fingertips_data(IndicatorID = 93107, AreaTypeID = 8) %>%
filter(AreaType == "Ward")
obese_year6_geo <- wards %>%
right_join(obese_year6_pct, by = c("wd18cd" = "AreaCode")) %>%
select(wd18cd, wd18nm, Value)## Warning: Column `wd18cd`/`AreaCode` joining factor and character vector,
## coercing into character vectortm_shape(obese_year6_geo) + tm_polygons(col = "Value")